Adaptive parameter selection for strategy adaptation in differential evolution
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Adaptive strategy selection in differential evolution for numerical optimization: An empirical study
Information Sciences: an International Journal
Enhancing the search ability of differential evolution through orthogonal crossover
Information Sciences: an International Journal
Adaptive population tuning scheme for differential evolution
Information Sciences: an International Journal
Differential evolution with a relational neighbourhood-based strategy for numerical optimization
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators
Information Sciences: an International Journal
A novel artificial bee colony algorithm with Powell's method
Applied Soft Computing
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Accelerating Gaussian bare-bones differential evolution using neighbourhood mutation
International Journal of Computing Science and Mathematics
Parameter optimization of PEMFC model with improved multi-strategy adaptive differential evolution
Engineering Applications of Artificial Intelligence
Repairing the crossover rate in adaptive differential evolution
Applied Soft Computing
Chaotic Evolution: fusion of chaotic ergodicity and evolutionary iteration for optimization
Natural Computing: an international journal
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Differential evolution (DE) is a simple, yet efficient, evolutionary algorithm for global numerical optimization, which has been widely used in many areas. However, the choice of the best mutation strategy is difficult for a specific problem. To alleviate this drawback and enhance the performance of DE, in this paper, we present a family of improved DE that attempts to adaptively choose a more suitable strategy for a problem at hand. In addition, in our proposed strategy adaptation mechanism (SaM), different parameter adaptation methods of DE can be used for different strategies. In order to test the efficiency of our approach, we combine our proposed SaM with JADE, which is a recently proposed DE variant, for numerical optimization. Twenty widely used scalable benchmark problems are chosen from the literature as the test suit. Experimental results verify our expectation that the SaM is able to adaptively determine a more suitable strategy for a specific problem. Compared with other state-of-the-art DE variants, our approach performs better, or at least comparably, in terms of the quality of the final solutions and the convergence rate. Finally, we validate the powerful capability of our approach by solving two real-world optimization problems.